/*
 * Encog(tm) Examples v2.4
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 * 
 * Copyright 2008-2010 by Heaton Research Inc.
 * 
 * Released under the LGPL.
 *
 * This is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2.1 of
 * the License, or (at your option) any later version.
 *
 * This software is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this software; if not, write to the Free
 * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
 * 02110-1301 USA, or see the FSF site: http://www.fsf.org.
 * 
 * Encog and Heaton Research are Trademarks of Heaton Research, Inc.
 * For information on Heaton Research trademarks, visit:
 * 
 * http://www.heatonresearch.com/copyright.html
 */

package crawler.web.engine.neural_network.classifier.feedforward;

import org.encog.normalize.DataNormalization;
import org.encog.persist.EncogPersistedCollection;
import org.encog.util.logging.Logging;

public class TagClassifier {

	public static void generate() {
		GenerateData generate = new GenerateData();
		//从mongodb到csv文件
		generate.step0();
		//segregates the data into training and evaluation files
		generate.step1();
		//balances the numbers of cover types
		generate.step2();
		//normalization
		DataNormalization norm = generate.step3();

		//将归一化策略存入持久文件中
		//saved to an Encog persistence file: forest.eg
		EncogPersistedCollection encog = new EncogPersistedCollection(Constant.TRAINED_NETWORK_FILE);
		//save norm as name forest-norm
		encog.add(Constant.NORMALIZATION_NAME, norm);
	}

	public static void train() {
		new TrainNetwork().train();
	}

	public static void evaluate() {
		new Evaluate().evaluate();
	}

	public static void main(String args[]) {
		Logging.stopConsoleLogging();
		generate();
		train();
		evaluate();
	}
}

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